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375 results for “Boreal forests”
Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest
<p>This data set is connected to the article: </p> <p>Jokinen, T., Lehtipalo, K., Thakur, R. C., Ylivinkka, I., Neitola, K., Sarnela, N., Laitinen, T., Kulmala, M., Petäjä, T., and Sipilä, M.: Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest, Atmos. Chem. Phys., 2022</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Data for "Vertical characterization of highly oxygenated molecules (HOMs) below and above a boreal forest canopy"
<p>This excel file consists of the data been analyzed in the manuscript "Vertical characterization of highly oxygenated molecules (HOMs) below and above a boreal forest canopy". For more details, please contact the author (qiaozhi.zha@helsinki.fi). </p>
Modified half-hourly FLUXNET dataset for 10 Boreal forest sites (CA-Obs,CA-Ojp,CA-Qfo,FI-Hyy,FI-Ken,FI-Let,FI-Sod,RU-Fyo,RU-Zot,US-Prr)
<p>This set contains half-hourly driving data and observations used in the simulations described in gmd-2018-313 (doi:10.5194/gmd-2018-313). Originally, this data is part of the FLUXNET2015 dataset (doi:10.17616/R36K9X). We have quality checked and gap-filled this data to suit the simulations.</p> <p>The upload contains site specific csv-files, a data header that is common to all files and a README. The actual data contains half-hourly values for:</p> <ul> <li>gross primary production (GPP, mol m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>evapotranspiration (ET, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>air temperature (air_temp, degrees celcius)</li> <li>air pressure (air_pressure, Pa)</li> <li>precipitation (precip, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>specific humidity (qair, kg<sup> </sup>kg<sup>-1</sup>)</li> <li>wind speed (wspeed, m s<sup>-1</sup>)</li> <li>CO2 concentration (CO2, mol mol<sup>-1</sup>)</li> <li>shortwave radiation (shortwave, W m<sup>-2</sup>)</li> <li>longwave radiation (longwave, W m<sup>-2</sup>)</li> <li>potential shortwave radiation (mpot, W m<sup>-2</sup>)</li> </ul> <p>The sites (named by their FLUXNET identifier) and the years of data in this set are:</p> <ul> <li>CA-Obs (Saskatchewan) 1999-2006</li> <li>CA-Ojp (Saskatchewan) 2004-2006</li> <li>CA-Qfo (Quebec) 2003-2010</li> <li>FI-Hyy (Hyytiälä) 1999-2006</li> <li>FI-Ken (Kenttärova) 2003-2010</li> <li>FI-Let (Lettosuo) 2010-2012</li> <li>FI-Sod (Sodankylä) 2001-2008</li> <li>RU-Fyo (Fyodorkovskoye) 2002-2009</li> <li>RU-Zot (Zotino) 2002-2004</li> <li>US-Prr (Poker Flat) 2011-2013</li> </ul>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
<p>Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.</p>
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
Biomass %N, %C, natural abundance 15N and 13C isotopic signatures for common and rare under- and overstory plants in long unburned and burned (1999) boreal forest stands, Caribou-Poker Creek and Delta Junction
This dataset contains leaf, aboveground stem, and fruit carbon and N concentration and natural abundance isotope data for new and old tissue fractions of common and rare plant species in burned and unburned black spruce forest stands. Stands were located in either Caribou-Poker Creek or Delta Junction, in either unburned areas, or in areas burned in 1999 fires. Biomass was collected between 2000 and 2001 in mid-July at peak biomass.
Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests
<p>This dataset includes all the data we collected at the first summer after biochar application in boreal forests. Our paper“ the effect of biochar on soil CO<sub>2</sub> efflux in boreal forests“ now is under review in Annals of Forest Science. Biochar prepared at two reaction temperatures was applied at three rates (including non-amended controls). During the first year after treatment, efflux increased with higher rates of biochar, but the reaction temperature had no effect. o explain char effects on efflux, soil moisture and temperature were also added to the model testing treatment effects. These environmental variables explained more of the variation in efflux and caused treatment to no longer have a significant effect. Based on this result, we concluded that soil temperature explains the effect of char on efflux.</p>
Identifying functional impacts of heat-resistant fungi on boreal forest recovery after wildfire
<p>Fungi play key roles in carbon (C) dynamics of ecosystems: saprotrophs decompose organic material and return C in the nutrient cycle, and mycorrhizal species support plants that accumulate C through photosynthesis. The identities and functions of extremophile fungi present after fire can influence C dynamics, particularly because plant-fungal relationships are often species-specific. However, little is known about the function and distribution of fungi that survive fires. We aim to assess the distribution of heat-resistant soil fungi across burned stands of boreal forest in the Northwest Territories, Canada, and understand their functions in relation to decomposition and tree seedling growth. We cultured and identified fungi from heat-treated soils and linked sequences from known taxa with high throughput sequencing fungal data (Illumina MiSeq, ITS1) from soils collected in 47 plots. We assessed functions under controlled conditions by inoculating litter and seedlings with heat-resistant fungi to assess decomposition and effects on seedling growth, respectively, for black spruce (Picea mariana), birch (Betula papyrifera), and jack pine (Pinus banksiana). We also measured litter decomposition rates and seedling densities in the field without inoculation. We isolated seven taxa of heat-resistant fungi and found their relative abundances were not associated with environmental or fire characteristics. Under controlled conditions, Fayodia gracilipes and Penicillium arenicola decomposed birch, but no taxa decomposed black spruce litter significantly more than the control treatment. Seedlings showed reduced biomass and/or mortality when inoculated with at least one of the fungal taxa. Penicillium turbatum reduced growth and/or caused mortality of all three species of seedlings. In the field, birch litter decomposed faster in stands with greater pre-fire proportion of black spruce, while black spruce litter decomposed faster in stands experiencing longer fire-free intervals. Densities of seedlings that had germinated since fire were positively associated with ectomycorrhizal richness while there were fewer conifer seedlings with greater heat-resistant fungal abundance. Overall, our study suggests that extremophile fungi present after fires have multiple functions and may have unexpected negative effects on forest functioning and regeneration. In particular, heat-resistant fungi after fires may promote shifts away from conifer dominance that are observed in these boreal forests.</p> <p> </p> <p> </p>
Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests
<p>This dataset includes all the data we collected at the first summer after biochar application in boreal forests. Our paper“ the effect of biochar on soil CO<sub>2</sub> efflux in boreal forests“ now is under review in Annals of Forest Science. Biochar prepared at two reaction temperatures was applied at three rates (including non-amended controls). During the first year after treatment, efflux increased with higher rates of biochar, but the reaction temperature had no effect. o explain char effects on efflux, soil moisture and temperature were also added to the model testing treatment effects. These environmental variables explained more of the variation in efflux and caused treatment to no longer have a significant effect. Based on this result, we concluded that soil temperature explains the effect of char on efflux.</p>
Dataset for "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland"
<p>This is the dataset (ver. 2017.02.13) for the manuscript "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland" submitted to the journal <em>Atmospheric Chemistry and Physics</em>.</p>
Data of LAI-L20C in Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM
<p>Data of LAI-L20C experiment in the research paper: Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM.</p> <p>The paper was submitted to JGR-Atmosphere.</p> <p>Variables are limited to those used in the paper.</p> <ul> <li>snow water equivalent (swe)</li> <li>snow cover fraction (snc)</li> <li>clear-sky downward shortwave radiation at surface (rsdscs)</li> <li>clear-sky upward shortwave radiation at surface (rsuscs)</li> <li>surface air temperature (tas)</li> </ul> <p>See the paper for the detail.</p>
Data set for "Drought response of the boreal forest carbon sink is driven by understory-tree composition"
<p>This data set is a compilation of 1) environmental conditions, 2) biometric- and chamber-based annual CO<sub>2</sub> fluxes, 3) vegetation phenological greenness, and 4) forest-floor environmental conditions, all measured over the Krycklan Catchment Study (KCS, <a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden.</p> <p>The environmental measurements cover the period 1991–2020. Specifically, meteorological conditions measured close to the central part of the KCS at the Svartberget reference climate station (64°14′N, 19°46′E, 225 m.a.s.l.) included air temperature at 1.7 m above ground (Ta, °C), global radiation at 1.7 m above ground (Rg, MJ m<sup>-2</sup>), and precipitation (P, mm). Drought conditions were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) computed at 3-month time scale. SPEI was retrieved from the 0.5° gridded dataset supplied in the Global SPEI Database (SPEIbase v2.8, <a href="https://spei.csic.es/database.html">https://spei.csic.es/database.html</a>). The data set comprises monthly values obtained during the long-term reference period 1991–2020 (LT<sub>91–20</sub>), the baseline period 2016–2017 (BL<sub>16–17</sub>), and the drought year 2018 (D<sub>18</sub>). The standardized anomaly (ɀ-score) was used to identify extreme environmental measurements during both the BL<sub>16–17 </sub>and D<sub>18</sub> periods relative to the LT<sub>91–20 </sub>period.</p> <p>Annual CO<sub>2</sub> flux estimates were collected in 50 forest stands located across the KCS during the period 2016–2018 using biometric- and chamber-based methods. However, to prevent confounding effects, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis. The selected forest stands encompassed different landscape attributes such as 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age classes (i.e., initiation, young, middle-aged, mature, and old-growth stands). The annual CO<sub>2</sub> fluxes included the net ecosystem production (NEP) and its component fluxes, i.e., net primary production (NPP), total heterotrophic respiration (RH), net primary production of trees (NPP<sub>t</sub>) and its above- and belowground components (ANPP<sub>t</sub> and BNPP<sub>t</sub>, respectively), and net primary production of understory (NPP<sub>u</sub>) and its above- and belowground components (ANPP<sub>u</sub> and BNPP<sub>u</sub>, respectively). The impact of drought on annual CO<sub>2</sub> fluxes was evaluated by calculating both the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>. To identify the temporal shift of the dominant contributor to ∆NEP, a moving-window correlation was conducted between the absolute anomaly of NEP (∆NEP) and the absolute anomalies of understory and tree NPP (∆NPP<sub>u</sub> and ∆NPP<sub>t</sub>, respectively), using a 7-forest-stand window with 1-forest-stand step.</p> <p>The study assessed the phenological greenness of the understory and trees in a ⁓110 years-old mixed-species forest stand in the central part of the KCS from 2016 to 2018. The greenness index (gcc) was derived from hourly images collected through digital repeat photography at the Integrated Carbon Observation System (ICOS) Svartberget ecosystem station (SE-Svb, 64°15′N, 19°46′E, 270 m.a.s.l., <a href="https://www.icos-sweden.se/svartberget">https://www.icos-sweden.se/svartberget</a>). Web cameras were used to capture images below- and above-tree canopy to define the gcc index for understory (gcc<sub>u</sub>) and trees (gcc<sub>t</sub>), respectively. The gcc<sub>u</sub> and gcc<sub>t</sub> values were then normalized (0–1) to describe the seasonal minimum and maximum of vegetation biomass development. A locally estimated scatterplot smoothing (loess) curve fit was then used through the normalized data points to improve visualization. The impact of drought on mean estimates of gcc<sub>u</sub> and gcc<sub>t</sub> during the growing season was evaluated by calculating the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>.</p> <p>Environmental conditions at the forest-floor interface were measured in each of the 50 forest stands located across the KCS during the period 2016–2018. As before, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis to prevent confounding effects. The measured conditions included the below-canopy air temperature (Ta<sub>bc</sub>, °C), soil temperature at 10 cm depth (Ts, °C), and soil volumetric water content at 5 cm depth (SWC, %). The data set includes mean monthly and mean May-August values estimated during the BL<sub>16–17</sub> and D<sub>18</sub> periods, for which the absolute and relative anomalies (∆X and δX, respectively) were calculated.</p> <p>This data set consists of four Microsoft Excel workbooks:</p> <p>1_dataset_environmental_conditions.xlxs</p> <p>2_dataset_biometric_&_chamber-based_CO2_fluxes.xlxs</p> <p>3_dataset_vegetation_phenological_greenness.xlxs</p> <p>4_dataset_forest-floor_environmental_conditions.xlxs</p> <p>Further details can be found in Martínez-García et al. “Drought response of the boreal forest carbon sink is driven by understory-tree composition” (Nature Geoscience, <a href="https://doi.org/10.1038/s41561-024-01374-9" target="_blank" rel="noopener noreferrer">https://doi.org/10.1038/s41561-024-01374-9</a>).</p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García<sup>1,2</sup> (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="eduardo.martinezgarcia@luke.fi">eduardo.martinezgarcia@luke.fi</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl<sup>1</sup> (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p><sup>1</sup> Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</p> <p><sup>2</sup> Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790, Helsinki, Finland</p>
A Peatland Sub-Class Map for the Canadian Boreal Forest
<p><strong>Authors: </strong><br>Pontone, N., Millard, K., Thompson, D. K., Guindon, L., Beaudoin A. (2024)</p> <p><br><strong>Contact:</strong><br>NicholasPontone@cmail.carleton.ca</p> <p> </p> <p><strong>Description:</strong><br>A map of peatland sub-classes (bog, poor fen, rich fen and permafrost peat complex) for the Canadian Boreal Forest circa 2020 created using a three-stage hierarchical classification framework. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L-band SAR and C-Band interferometric SAR coherence, forest structure, and ancillary variables were used as model predictors. Ancillary data were used to mask agricultural areas and urban regions, and account for regions that may exhibit permafrost</p> <p><br><strong>Pixel Values:</strong></p> <p>1: Bog<br>2: Rich Fen<br>3: Poor Fen<br>4: Peatland Permafrost Complex<br>5: Mineral Wetlands<br>6: Water<br>7: Upands<br>8: Agriculture<br>9: Urban</p> <p><br><strong>Recommended Colours</strong></p> <p>1: 4C0073<br>2: FFFF00<br>3: E64C00<br>4: 727272<br>5: F4C2C2<br>6: 0070FF<br>7: 4C7300<br>8: 623131<br>9: 000000</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Pontone, N., Millard, K., Thompson, D.K., Guindon, L. and Beaudoin, A. (2024), A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.384</p> <p> </p> <p>This data was released in combination with PALSAR-2 L-band dual-polarized radar backscatter summer composites (circa 2020). </p> <p>Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. <a href="https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209" target="_blank" rel="nofollow noopener">https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209</a></p> <p>The peatland map is also available as a Google Earth Engine asset (projects/ee-peatlandthesis/assets/PeatlandMap8b_2023_07_17). </p> <p> </p>
Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Mineral Soils of a Boreal Forest Ecosystem
<p><span>Files for the manuscript “</span><span>Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a </span><span>Boreal</span><span> Forest Ecosystem”</span></p> <p> </p> <p>1. “Raw_Data” folder contains the files in .xlsx:</p> <p>- Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights.</p> <p>- Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling<span> </span>campaign.</p> <p>- Lab_Solid_Samples:<span> </span>D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.</p>
Long-term litter fall data series from 34 boreal forest stands in Finland
<p><strong>Introduction</strong></p> <p>Litter fall data were collected on a network of 34 forest sampling plots in Finland from late 1950s to 2010s. The data collection spanned different time periods in different sampling plots. The data have been used for studies on the flowering and seed crop of forest trees, air quality, and insect damage (see list of publications in the end of this document). They have been used to develop seed production and needle litter fall models for Scots pine (<em>Pinus sylvestris</em>) and Norway spruce (<em>Picea abies</em>), a branch litter model for pine, and total litter fall models used in greenhouse gas inventories.</p> <p><strong>Data collection</strong></p> <p>The litter fall collection was set up in mature, single species stands. The focal tree species include Scots pine (<em>Pinus sylvestris</em>), Norway spruce (<em>Picea abies</em>), Silver birch (<em>Betula pendula</em>), Downy birch (<em>Betula pubescens</em>), Grey alder (<em>Alnus</em> <em>incana</em>), European rowan (<em>Sorbus aucuparia</em>), European larch (<em>Larix decidua</em>), and Siberian larch (<em>Larix sibirica</em>). The sampling plots varied in shape and in size with a typical area of 0.1-0.25 ha. Between 6 and 30 litter collecting funnels were used per plot. The funnels were made of galvanized sheet metal and attached to cloth bags to collect the falling litter. The sampling sites were monitored for changes in conditions, such as natural disturbances, tree harvesting, forestry operations, or construction on the plot or in its immediate proximity (none observed).</p> <p>The litter samples were collected from the sampling plots, usually four to six times per year in spring to autumn. The samples were dried in room temperature (except male flowers in 1960s – 1970s, see note in Table 1) and stored in paper bags. The dried samples were sorted into litter fractions (Table 1). These fractions varied between tree species and between years to some extent. Cones and seeds were counted, and all other litter fractions were weighed to the nearest milligram.</p> <table> <caption>Table 1. Litter fractions and their codes. The code of the litter fraction is used in the data files.</caption> <thead> <tr> <th scope="col">Code</th> <th scope="col">Litter fraction</th> <th scope="col">Description / note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Male flowers</td> <td>In the 1960s – 1970s male flowers were dried in 105°C for 24 hours (Sarvas 1962, 1968).</td> </tr> <tr> <td>2</td> <td>Seeds</td> <td>Seed wings and seeds from species other than the focal one were included into the “other litter” fraction.</td> </tr> <tr> <td>3</td> <td>Female flowers</td> <td> </td> </tr> <tr> <td>4</td> <td>Needles</td> <td> </td> </tr> <tr> <td>5</td> <td>Insects and their faeces</td> <td> </td> </tr> <tr> <td>6</td> <td>Other litter</td> <td> </td> </tr> <tr> <td>7</td> <td>Lichens, branches, and tree bark</td> <td>In some years lichens, branches, and bark were combined in the same fraction, and in some they were separated in their own fractions (numbers 12-14 below).</td> </tr> <tr> <td>8</td> <td>Small cones (1 year)</td> <td>For pine stands, cones were separated into one-year old small cones and large cones. Cones were counted.</td> </tr> <tr> <td>9</td> <td>Cones</td> <td> </td> </tr> <tr> <td>10</td> <td>Cones and loose scales</td> <td>In some years loose scales were included in the same fraction as cones, and in some they were included into the “other litter” fraction.</td> </tr> <tr> <td>11</td> <td>Shifting dust</td> <td> </td> </tr> <tr> <td>12</td> <td>Branches</td> <td> </td> </tr> <tr> <td>13</td> <td>Lichens</td> <td> </td> </tr> <tr> <td>14</td> <td>Tree bark</td> <td> </td> </tr> <tr> <td>15</td> <td>Leaves</td> <td>For deciduous stands, leaves were separated into small (diameter < 1 cm) and large leaves (diameter > 1 cm), while for conifer stands all leaves were included into the “other litter” fraction.</td> </tr> <tr> <td>16</td> <td>Berries</td> <td> </td> </tr> </tbody> </table> <p>In addition to litter fall data, tree stand data were collected on most of the plots in some years. In the tree stand inventories, all trees in the sampling plot with diameter at breast height ≥ 7 cm were mapped, and all trees were counted and measured for diameter (at breast height and at 6 meters), total height, and height to first living branches. Stand basal area and dominant diameter and height were calculated. Stand age was estimated based on core samples from five trees outside but representative of the sample plot. Crown coverage was estimated with a Cajanus tube.</p> <p><strong>Description of the data files</strong></p> <p>The litter fall data is in eight csv-files, one per tree species. The files are named “Litter_Tree_species.csv”, for example “Litter_Betula_pendula.csv”.</p> <p>Variables (in columns) are consistent across the files (explained in Table 2), but note that there are varying numbers of columns between the tables in the files, as each litter collecting funnel has its own column and different maximum numbers of funnels were used in different sampling sites and tree species (see row “S1 – S30” in Table 2 for more details).</p> <table> <caption>Table 2. Variables included in the litter fall data files.</caption> <thead> <tr> <th scope="col">Variable name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Form</td> <td>Number identifying the original paper form.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Tree species code: 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch, 5 = Grey alder, 6 = Siberian larch, 7 = European larch, 8 = European rowan.</td> </tr> <tr> <td>LitterFraction</td> <td>Code for the litter fraction (1-16), explained in Table 1.</td> </tr> <tr> <td>Coefficient</td> <td>Coefficient used to transform the weight of the litter (g) to weight per square meter (g m<sup>-2</sup>). The coefficient is based on the number and area of the collection funnels.</td> </tr> <tr> <td>Date</td> <td>Date of sample collection.</td> </tr> <tr> <td>Period</td> <td>Variable used to define the time of data collection as calendar year or phenological year. The variable is based on the schedule of data collection in different years and on the focal tree species so that it corresponds to the species-specific litter fall schedule. For spruce, the peak needle fall is in the spring, so the calendar year is appropriate for describing the temporal variation in litter fall. For pine, the peak needle fall is in August–September, so a phenological year defined as July 1<sup>st</sup> – June 30<sup>th</sup> is appropriate for describing the temporal variation in litter fall. Period = -1 means the values in the row are allocated to the previous calendar year; period = 0 means the values are allocated to the current calendar year; and period = 1 means the values are allocated to the next calendar year.</td> </tr> <tr> <td>S1 – S30</td> <td>Columns S1 – S30 refer to litter collection funnels 1 – 30. The maximum number of funnels varies between tree species: for <em>silver birch</em> up to 30 funnels were used per plot, for downy birch up to 20 funnels, for spruce up to 10 funnels, for pine up to 15 funnels, for rowan 10 funnels, and for both larch species and alder 8 funnels. Missing values (NA) mean that the funnel was not used in the plot. Value -1 means that the funnel was used but the sample was missing. The values give the dry weight of the litter in mg.</td> </tr> <tr> <td>Combined</td> <td>For some samples, litter collected by different funnels has been combined and the total weight is shown in column S1. In this column, 0 = values have not been combined, and 1 = values have been combined.</td> </tr> <tr> <td>TotalWeight</td> <td>Total weight of the litter (mg).</td> </tr> <tr> <td>TotalWeightArea</td> <td>Total weight of the litter per area (mg m<sup>-2</sup>). Value is same as TotalWeight × Coefficient.</td> </tr> <tr> <td>Note</td> <td>Note</td> </tr> </tbody> </table> <p>Tree stand data is in one csv-file, named “Tree_stand_data.csv”, and can be combined with the litter fall data based on the sample plot abbreviations (variable “PlotAbbr” in both litter fall data files and the tree stand data file). Note that there is no tree stand data available for all the same years as litter fall data. There is no tree stand data available at all for one downy birch site (abbreviation HEI568), one spruce site (NOO85), one pine site (HEI566), and the alder, rowan, and larch sites. Tree stand data variables are explained in Table 3.</p> <table> <caption>Table 3. Variables included in the tree stand data file.</caption> <tbody> <tr> <td>Variable name</td> <td>Description</td> </tr> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Dominant tree species. 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch.</td> </tr> <tr> <td>Age</td> <td>Stand age (years).</td> </tr> <tr> <td>SiteType</td> <td>Forest site type describing site productivity. 2 = xeric heath forest, 3 = sub-xeric heath forest, 4 = mesic heath forest, 5 = herb-rich heath forest. </td> </tr> <tr> <td>North</td> <td>North coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>East</td> <td>East coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>Elevation</td> <td>Elevation (m above sea level).</td> </tr> <tr> <td>N</td> <td>Stem number (ha<sup>-1</sup>).</td> </tr> <tr> <td>BA</td> <td>Basal area (m<sup>2</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>DomD</td> <td>Diameter (cm) of dominant trees.</td> </tr> <tr> <td>DomH</td> <td>Height (m) of dominant trees.</td> </tr> <tr> <td>CrownLength</td> <td>Crown length (m).</td> </tr> <tr> <td>V</td> <td>Stem volume (m<sup>3</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>CrownCover</td> <td>Crown coverage (%).</td> </tr> </tbody> </table> <p> </p> <p><strong>List of publications </strong></p> <p>Hilli, A., Hokkanen, T., Hyvönen, J. & Sutinen, M.-L. 2008. Long-term variation in Scots pine seed crop size and quality in northern Finland. Scandinavian Journal of Forest Research 23(5): 395-403. </p> <p>Koski, V. & Tallqvist, R. (1978). Results of long-time measurements of the quantity of flowering and seed crop of forest trees (in Finnish with English summary). Folia Forestalia, 364, 1-60. </p> <p>Kouki, J. & Hokkanen, T. 1992. Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors. Oecologia 89: 176-181. </p> <p>Lehtonen, A., Lindholm, M., Hokkanen, T., Salminen, H. & Jalkanen, R. 2008. Testing dependence between growth and needle litterfall in Scots pine - a case study in northern Finland. Tree Physiology 28(11): 1741-1749. </p> <p>Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K.T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling 180(2-3): 305-315. </p> <p>Leikola, M., Raulo, J. & Pukkala, T. (1982). Prediction of the variations of the seed crop of Scots pine and Norway spruce (in Finnish with English summary). Folia Forestalia, 537, 1-43. </p> <p>Niemistö, P., Hokkanen T. & Varama, M. 2004. Karikemäärän muutokset 1982–2001 ja puiden kunto lumi- ja hallamittariesiintymän vaivaamissa koivikoissa Noormarkussa. Metsätieteen aikakauskirja 1/2004: 21–41. </p> <p>Poikolainen, J. & Kuusinen, M. 2000. Abundance of epiphytic lichens in litterfall during 1967-1994. In: Forest condition in a changing environment - the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers / Ed. Mälkönen, E. Sivut: 171-172. </p> <p>Pukkala, T. 1987a. A model for predicting the seed crop of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 135-144. </p> <p>Pukkala, T. 1987b. Effect of seed production on the annual growth of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 145-158. </p> <p>Pukkala, T., Hokkanen, T. & Nikkanen, T. 2010. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fennica 44(4): 629-642. </p> <p>Ranta, E., Lindström, J., Kaitala, V., Crone, E., Lundberg, P., Hokkanen, T. & Kubin, E. 2010. Life history mediated responses to weather, phenology and large-scale population patterns. In: Hudson, I. L & Keatley, M. R. (eds.). Phenological Research. Springer, Dordrecht Heidelberg London New York, Netherlands. p. 321-338. </p> <p>Raulo, J. & Hokkanen, T. 1989. Litter fall of Alnus incana and Alnus glutinosa (in Finnish with English summary). Folia Forestalia 738. 25 s.</p> <p>Saarsalmi, A., Starr, M., Hokkanen, T., Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. 2007. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 242(2-3): 578-586. </p> <p>Sarvas, R. (1962). Investigations on the flowering and seed crop of Pinus Silvestris. Communicationes Instituti Forestalis Fenniae, 53, 1-198. </p> <p>Sarvas, R. 1968. Investigation on the flowering and seed crop of Picea abies. Communicationes Instituti Forestalis Fenniae 67.5. 84 pp. </p> <p>Starr, M., Saarsalmi, A., Hokkanen, T., Merilä, P. & Helmisaari, H.-S. 2005. Models of litterfall production for Scots pine (Pinus sylvestris L.) in Finland using stand, site and climate factors. Forest Ecology and Management 205: 215-225. </p> <p>Ťupek, B., Mäkipää, R., Heikkinen J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M. & Lehtonen, A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Environment Research 20: 283–304</p>
Dataset for "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest"
<p>The final dataset used in manuscript "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest" by Vesala et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as meteorological drivers for SiB4 simulations and SiB4 simulated COS flux at the Hyytiälä grid cell from January 2013 to December 2017. Raw data are available upon request from the author.</p>
Rebuilding green infrastructure in boreal production forest given future global wood demand
<p>Global policy for future biodiversity conservation is ultimately implemented at landscape and local scales. In parallel, green infrastructure (GI) planning needs to account for socio-economic dynamics at national and global scales. Progress towards policy goals must, in turn, be evaluated at the landscape scale. Evaluation tools are often environmental quality objectives (EQO) indicators.</p> <p>We present three management scenarios for a 100,000 hectare boreal forest landscape in Sweden in the coming 100 years. The scenarios optimize financial returns and account for downscaled projected global demand of wood given a middle-of-the road Shared Socioeconomic Pathway (SSP2). We contrast a <em>reference</em> scenario meeting the wood demand against an <em>economy</em> scenario with no upper harvest limit, and a <em>green infrastructure</em> (<em>GI</em>) scenario optimizing the levels of four EQO indicators (the area of old forest, the area of mature broadleaf-rich forest, the amount of deadwood and the density of large trees).</p> <p>EQO indicators generally reached the highest levels in the <em>GI</em> scenario and the lowest levels in the <em>economy</em> scenario. Most indicators increased further in set-asides. The financial profit was 14% lower in the <em>GI</em> and 2% higher in the <em>economy</em> than in the <em>reference</em> scenario.</p> <p>These scenarios were used in the associated publication to evaluate the future response of eleven model species from three different species groups with widely differing habitat requirements. The studied species were four bird species, six wood-decaying fungi and one lichen, all either of conservation concern or considered indicator species for forest of high conservation value. Models and data for the birds and fungi have been published previously. The model for the lichen <em>Lobaria pulmonaria</em> was created for this study; the underlying data is therefore presented here as well.</p> <p>Our study has shown that effects of global SSPs can be downscaled and accounted for in planning landscape-scale forest and conservation management. Accounting for EQO indicators in the management optimization was found to be an effective approach to reveal scenarios for reaching targets on both revenue and conservation. Rebuilding green infrastructure in the production forest is possible at a relatively minor economic cost and to the benefit of species of conservation concern.</p>
Partial cutting of a boreal nutrient-rich peatland forest causes radically less on-site CO2 emissions than clear-cutting
<p>This package contains the data used in the research article: "Partial cutting of a boreal nutrient-rich peatland forest causes radically less on-site CO2 emissions than clear-cutting" published in Agricultural and Forest Meteorology.</p> <p>LAI_data.xlsx - Contains Leaf Area Index data and their standard deviations for all the measured areas</p> <p>WTL_data.csv - Contains the mean water table level data for pre-harvest, partial harvest and clearcut areas.</p> <p>Lettosuo_2010-2015_Section_A_fluxes.csv - Contains the pre-harvest (2010-2015) carbon flux data for Section A.</p> <p>Lettosuo_2010-2015_Section_BCD_fluxes.csv - Contains the pre-harvest carbon flux data for Section BCD.</p> <p>Lettosuo_2016-2021_Section_AB_(partialcut).csv - Contains the carbon flux data for the partial cut area (2016-2021, Section AB).</p> <p>Lettosuo_2016-2021_Section_D_(Clearcut).csv - Contains the carbon flux data for the clear-cut area (2016-2021, Section D)</p> <p>The carbon flux data files contain the following columns:</p> <p>Gapfilled PAR - Gapfilled photsynthetically active radiation</p> <p>Gapfilled air temperature - Gapfilled air temperature</p> <p>Measured NEE - Filtered NEE data</p> <p>Modelled TER - Modelled total ecosystem respiration</p> <p>Modelled GPP - Modelled gross primary production</p> <p>Modelled NEE - Modelled NEE calculated from the modelled TER and GPP</p> <p>Gapfilled NEE - A combination of measured and modelled NEE. Gaps in the measured data are filled with modelled NEE</p> <p>Modelling uncertainty - Uncertainty of the modelled NEE</p> <p>Measurement uncertainty - An estimation of the uncertainty of the measured NEE</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.